Performance Analysis of Automatic Modulation Classification using Time Frequency Transforms under Non-Ideal Channel Conditions
M.Venkata Subbarao1, P.Samundiswary2
1M.Venkata Subbarao*, Department of ECE, Shri Vishnu Engineering College for Women, A.P, India.
2P.Samundiswary, Department of EE, School of Engg. & Tech., Pondicherry University, India.
Manuscript received on September 17, 2019. | Revised Manuscript received on 25 September, 2019. | Manuscript published on October 10, 2019. | PP: 1685-1691 | Volume-8 Issue-12, October 2019. | Retrieval Number: L31711081219/2019©BEIESP | DOI: 10.35940/ijitee.L3171.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification of different analog and digital modulation classes using Time-Frequency Transforms (TFTs) through MST and MFSWT under ideal channel conditions is presented in this paper. It also deals with performance analysis of proposed Modified S- Transform (MST) and Modified Frequency Slice Wavelet Transform (MFSWT) based Automatic Modulation Classification (AMC) methods under different channel conditions such as Gaussian and fading channels. The performance of the proposed TFT based AMC methods under AWGN (with SNR values varied from -10 dB to 20 dB) and fading channels is examined through simulation. Moreover, the performance of the proposed TFT based AMC is compared with that of the existing techniques in terms of performance metric namely classification accuracy which is also discussed in this paper.
Keywords: S-Transform, Fading Channels, AWGN, FSWT, Cognitive Radio, SDR
Scope of the Article: Classification